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First published online January 1, 2013

Feasibility and Advantages of Estimating Local Road Vehicle Miles Traveled on Basis of Global Positioning System Travel Data

Abstract

The Highway Performance Monitoring System and the reporting of vehicle miles traveled (VMT) on different levels of roadways are critical parts of the U.S. national transportation data program. Although the amount of travel on higher-level roads can often be reliably estimated from traffic counts and other data sources, existing heuristic methods for estimating lower-level and local road VMT suffer from the lack of ground truth data. This paper reports the development of a novel method for estimating local road VMT on the basis of the Global Positioning System (GPS) and other supplemental data sources and investigates the associated statistical issues. The proposed method is applicable at the national, state, and local levels and is demonstrated in a case study in Maryland. The size and duration of the GPS survey sample required for reliable VMT estimation were also analyzed. The case study and statistical analysis showed that a 30-day GPS survey would reduce the required sample size by approximately 50% to 60% as compared with a single-day survey and that a 15-day GPS survey with 670 participating drivers could provide local road VMT estimates with a 5% margin of error at the 95% confidence level. Survey designers can either reduce sample size by lengthening the duration of surveys or recruit more participants for a shorter survey. These findings suggest GPS-based surveys are feasible and cost-effective options for VMT estimation on different levels of roadways, including local roads. Federal, state, and local agencies may use GPS surveys already planned for other purposes (e.g., travel demand modeling and planning applications) for VMT estimation.

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Article first published online: January 1, 2013
Issue published: January 2013

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Authors

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Lei Zhang
1173 Glenn Martin Hall, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742.
Xiang He
3246 Jeong H. Kim Engineering Building, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742.

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